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中文摘要：
文中提出了一种基于类间距判据的高斯过程分类（GPC）模型核参数选择方法.将核参数作为自变量,类间距作为因变量,获得类间距随核参数变化的目标函数,然后采用共轭梯度法求取目标函数极值,最终获得核参数的最优值.实验表明,用DBTC作为判据进行核参数选择,分类正确率与原有参数选择方法基本相当,但GPC模型在进行参数选择时的耗时大幅减少,因而模型训练速度得到大幅提升.
英文摘要：
This paper proposes a new kernel parameter selection method of GPC model based on DBTC criterion. After the kernel parameter is used as independent variable, and the DBTC is used as induced variable, we obtain object function that DBTC is varied with kernel parameter. Following that, conjugate gradient method is utilized to calculate the exterma of object function. Finally, the optimal value of kernel parameter is obtained. Experiments illustrates that the proposed method achieved comparable classification accuracy to traditional method. However, the time consuming in parameter section is sharply shortened. Consequently, the training speed of GPC model is improved.
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